Walaa Hamouda

Ultra-Dense Networks: A Survey

The exponential growth and availability of data in all forms is the main booster to the continuing evolution in the communications industry. The popularization of traffic-intensive applications including high definition video, 3-D visualization, augmented reality, wearable devices, and cloud computing defines a new era of mobile communications. The immense amount of traffic generated by todays customers requires a paradigm shift in all aspects of mobile networks. Ultradense network (UDN) is one of the leading ideas in this racetrack. In UDNs, the access nodes and/or the number of communication links per unit area are densified. In this paper, we provide a survey-style introduction to dense small cell networks. Moreover, we summarize and compare some of the recent achievements and research findings. We discuss the modeling techniques and the performance metrics widely used to model problems in UDN. Also, we present the enabling technologies for network densification in order to understand the state-of-the-art. We consider many research directions in this survey, namely, user association, interference management, energy efficiency, spectrum sharing, resource management, scheduling, backhauling, propagation modeling, and the economics of UDN deployment. Finally, we discuss the challenges and open problems to the researchers in the field or newcomers who aim to conduct research in this interesting and active area of research.

A Water-Filling Based Scheduling Algorithm for the Smart Grid

The processing and communication capabilities of the smart grid provide a solid foundation for enhancing its efficiency and reliability. These capabilities allow utility companies to adjust their offerings in a way that encourages consumers to reduce their peak hour consumption, resulting in a more efficient system. In this paper, we propose a method for scheduling a communitys power consumption such that it becomes almost flat. Our methodology utilizes distributed schedulers that allocate time slots to soft loads probabilistically based on precalculated and predistributed demand forecast information. This approach requires no communication or coordination between scheduling nodes. Furthermore, the computation performed at each scheduling node is minimal. Obtaining a relatively constant consumption makes it possible to have a relatively constant billing rate and eliminates operational inefficiencies. We also analyze the fairness of our proposed approach, the effect of the possible errors in the demand forecast, and the participation incentives for consumers.

Automatic modulation recognition using wavelet transform and neural networks in wireless systems

Modulation type is one of the most important characteristics used in signal waveform identification. In this paper, an algorithm for automatic digital modulation recognition is proposed. The proposed algorithm is verified using higher-order statistical moments (HOM) of continuous wavelet transform (CWT) as a features set. A multilayer feed-forward neural network trained with resilient backpropagation learning algorithm is proposed as a classifier. The purpose is to discriminate among different M-ary shift keying modulation schemes and the modulation order without any priori signal information. Pre-processing and features subset selection using principal component analysis is used to reduce the network complexity and to improve the classifiers performance. The proposed algorithm is evaluated through confusion matrix and false recognition probability. The proposed classifier is shown to be capable of recognizing the modulation scheme with high accuracy over wide signal-to-noise ratio (SNR) range over both additive white Gaussian noise (AWGN) and different fading channels.

A Convolutional-Based Distributed Coded Cooperation Scheme for Relay Channels

In this paper, we consider a coded cooperation diversity scheme that is suitable for L-relay channels that operate in the decode-forward mode. The proposed scheme is based on convolutional coding, where each codeword of the source node is partitioned into two frames that are transmitted in two phases. In the first phase, the first frame is broadcast from the source to the relays and destination. In the second phase, the second frame is transmitted on orthogonal subchannels from the source and relay nodes to the destination. Each relay is assumed to be equipped with a cyclic redundancy check (CRC) code for error detection. Only these relays (whose CRCs check) transmit in the second phase. Otherwise, they keep silent. At the destination, the received replicas (of the second frame) are combined using maximal ratio combining. The entire codeword, which comprises the two frames, is decoded via the Viterbi algorithm. We analyze the proposed scheme in terms of its probability of bit error and outage probability. Explicit upper bounds are obtained, assuming M-ary phase-shift keying transmission. Our analytical results show that the full diversity order is achieved, provided that the source-relay link is more reliable than the other links. Otherwise, the diversity degrades. However, in both cases, it is shown that it is possible to achieve substantial performance improvements over noncooperative coded systems. Several numerical and simulation results are presented to demonstrate the efficacy of the proposed scheme.

Blind Digital Modulation Identification for Spatially-Correlated MIMO Systems

Modulation type is one of the most important characteristics used in signal waveform identification and classification. Spatial correlation is a crucial factor for practical multiple-input multiple-output (MIMO) systems. This paper addresses the problem of blind digital modulation identification in spatially-correlated MIMO systems. The proposed algorithm is verified using higher order statistical moments and cumulants of the received signal. The purpose is to discriminate among different M-ary shift keying linear modulation schemes without any priori signal information. This study employs several MIMO techniques to identify the modulation with and without channel state information (CSI). The proposed classifier shows a high identification performance in acceptable signal-to-noise ratio (SNR) range.

Next generation M2M cellular networks: challenges and practical considerations

In this article we present the major challenges of future machine-to-machine (M2M) cellular networks such as spectrum scarcity, and support for a large number of low-power, lowcost devices. As an integral part of the future Internet-of-Things (IoT), the true vision of M2M communications cannot be reached with conventional solutions that are typically cost inefficient. The cognitive radio concept has emerged to address spectrum under-utilization and scarcity. The heterogeneous network model is another alternative to relax the number of covered users. To this extent, we present a complete fundamental understanding and the engineering details of cognitive radios, the heterogeneous network model, and power and cost challenges in the context of future M2M cellular networks.

Advances on Spectrum Sensing for Cognitive Radio Networks: Theory and Applications

Due to the under-utilization problem of the allocated radio spectrum, cognitive radio (CR) communications have recently emerged as a reliable and effective solution. Among various network models, this survey paper focuses on the enabling techniques for interweave CR networks which have received great attention from standards perspective due to its reliability to achieve the required quality-of-service. Spectrum sensing provides the essential information to enable this interweave communications in which primary and secondary users are not allowed to access the medium concurrently. Several researchers have already considered various aspects to realize efficient techniques for spectrum sensing. In this direction, this survey paper provides a detailed review of the state-of-the-art related to the application of spectrum sensing in CR communications. Starting with the basic principles and the main features of interweave communications, this paper provides a classification of the main approaches based on the radio parameters. Subsequently, we review the existing spectrum sensing works applied to different categories such as narrowband sensing, narrowband spectrum monitoring, wideband sensing, cooperative sensing, practical implementation considerations for various techniques, and the recent standards that rely on the interweave network model. Furthermore, we present the latest advances related to the implementation of the legacy spectrum sensing approaches. Finally, we conclude this survey paper with some suggested open research challenges and future directions for the CR networks in next generation Internet-of-Things applications.

Cross-Layer Antenna Selection and Channel Allocation for MIMO Cognitive Radios

We propose algorithms to address the spectrum efficiency and fairness issues of multi band multiuser Multiple-Input and Multiple-Output (MIMO) cognitive ad-hoc networks. To improve the transmission efficiency of the MIMO system, a cross layer antenna selection algorithm is proposed. Using the transmission efficiency results, user data rate of the cognitive ad-hoc network is determined. Objective function for the average data rate of the multi band multiuser cognitive MIMO ad-hoc network is also defined. For the average data rate objective function, primary users interference is considered as performance constraint. Furthermore, using the user data rate results, a learning-based channel allocation algorithm is proposed. Finally, numerical results are presented for performance evaluation of the proposed antenna selection and channel allocation algorithms.

Cross-layer based transmit antenna selection for decision-feedback detection in correlated Ricean MIMO channels

In this paper, we investigate a cross-layer transmit antenna selection (AS) approach for the decision-feedback detector (DFD) over spatially correlated flat Rician fading multiple-input multiple-output (MIMO) channels. Closed-form expressions for the system throughput with both perfect and imperfect channel estimation are derived. Considering a training-based channel estimation technique, we show that the capacity-based AS is more robust to imperfect channel estimation. However, in all cases, the cross-layer AS delivers higher throughput gains than the capacity-based AS.

Decode-Compress-and-Forward with Selective-Cooperation for Relay Networks

We propose a new signal-processing scheme, referred to as Decode-Compress-and-Forward with Selective-Cooperation (DCF-SC). In DCF-SC, the relay dedicates a certain amount of time to listen to the message broadcasted by the source and then performs Soft-Input Soft-Output (SISO) decoding. The relay then quantizes the Log-Likelihood Ratio (LLR) values received from the SISO decoder, encodes them and then transmits to the destination. The Selective-Cooperation condition determines whether the destination will accept or reject relays collaboration. We consider half-duplex relaying with orthogonal channels at the destination and apply turbo coding at both source and relay nodes. We define a trade-off parameter that determines how much of the relays time should be dedicated to listening and to transmission. We show by simulations that this trade-off factor has an optimal value for which the Block-Error Rate (BLER) is minimized. We compare the error rate performance of the proposed DCF-SC scheme with that of the Decode-Amplify-Forward (DAF) scheme presented in the literature.